Data Augmentation Vision Transformer for Fine-grained Image Classification

Recently, the vision transformer (ViT) has made breakthroughs in image recognition. Its self-attention mechanism (MSA) can extract discriminative labeling information of different pixel blocks to improve image classification accuracy. However, the classification marks in their deep layers tend to ignore local features between layers. In addition, the embedding layer will be fixed-size pixel blocks. Input network Inevitably introduces additional image noise. To this end, we study a data augmentation vision transformer (DAVT) based on data augmentation and proposes a data augmentation method for attention cropping, which uses attention weights as the guide to crop images and improve the ability of the network to learn critical features. Secondly, we also propose a hierarchical attention selection (HAS) method, which improves the ability of discriminative markers between levels of learning by filtering and fusing labels between levels. Experimental results show that the accuracy of this method on the two general datasets, CUB-200-2011, and Stanford Dogs, is better than the existing mainstream methods, and its accuracy is 1.4\% and 1.6\% higher than the original ViT, respectivelyComment: IEEE Signal Processing Letter

Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization

Vision transformers (ViTs) have been successfully deployed in a variety of computer vision tasks, but they are still vulnerable to adversarial samples. Transfer-based attacks use a local model to generate adversarial samples and directly transfer them to attack a target black-box model. The high efficiency of transfer-based attacks makes it a severe security threat to ViT-based applications. Therefore, it is vital to design effective transfer-based attacks to identify the deficiencies of ViTs beforehand in security-sensitive scenarios. Existing efforts generally focus on regularizing the input gradients to stabilize the updated direction of adversarial samples. However, the variance of the back-propagated gradients in intermediate blocks of ViTs may still be large, which may make the generated adversarial samples focus on some model-specific features and get stuck in poor local optima. To overcome the shortcomings of existing approaches, we propose the Token Gradient Regularization (TGR) method. According to the structural characteristics of ViTs, TGR reduces the variance of the back-propagated gradient in each internal block of ViTs in a token-wise manner and utilizes the regularized gradient to generate adversarial samples. Extensive experiments on attacking both ViTs and CNNs confirm the superiority of our approach. Notably, compared to the state-of-the-art transfer-based attacks, our TGR offers a performance improvement of 8.8% on average.Comment: CVPR 202

A Lightweight Reconstruction Network for Surface Defect Inspection

Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics. This paper proposes an unsupervised defect detection algorithm based on a reconstruction network, which is realized using only a large number of easily obtained defect-free sample data. The network includes two parts: image reconstruction and surface defect area detection. The reconstruction network is designed through a fully convolutional autoencoder with a lightweight structure. Only a small number of normal samples are used for training so that the reconstruction network can be A defect-free reconstructed image is generated. A function combining structural loss and $\mathit{L}1$ loss is proposed as the loss function of the reconstruction network to solve the problem of poor detection of irregular texture surface defects. Further, the residual of the reconstructed image and the image to be tested is used as the possible region of the defect, and conventional image operations can realize the location of the fault. The unsupervised defect detection algorithm of the proposed reconstruction network is used on multiple defect image sample sets. Compared with other similar algorithms, the results show that the unsupervised defect detection algorithm of the reconstructed network has strong robustness and accuracy.Comment: Journal of Mathematical Imaging and Vision(JMIV

Analysis of molecular mechanisms of drug resistance of Mycobacterium tuberculosis in patients with pulmonary tuberculosis and its pharmacoeconomics

Purpose: To investigate the molecular mechanisms of drug resistance of Mycobacterium tuberculosis in patients with pulmonary tuberculosis and its pharmacoeconomics. Methods: Data pertaining to patients with primary tuberculosis treated in the First Affiliated Hospital of Zhaoqing Medical College, Zhaoqing, China from January 2020 to June 2021 were retrospectively analyzed. Sputum specimens were collected from all eligible patients, and 151 uncontaminated specimens with good bacteriophage activity were screened. Results: A total of 107 Mycobacterium tuberculosis strains were isolated from the 151 specimens, 31 of which strains were resistant to varying degrees to rifampicin, isoniazid, streptomycin, and ethambutol with an overall resistance of 28.97 %. There were 16 strains with rpoB mutation, 22 strains with katG mutation, and 8 strains with inhA mutation. The difference in the sputum negative rate, lesion absorption rate, and tuberculosis cavity closure rate, and total medical cost between the two group were not statistically significant (p > 0.05). The incidence of adverse reactions in the FDC group was significantly lower than that in the blister pack group (p < 0.05). Conclusion: The total resistance of Mycobacterium tuberculosis in primary tuberculosis patients remains at a high level, and the development of resistance is associated with mutations in rpoB, katG, and inhA genes. FDC regimen provides more pharmacoeconomic and therapeutic benefits than blister pack regimen

Do Not Give Away My Secrets: Uncovering the Privacy Issue of Neural Code Completion Tools

Neural Code Completion Tools (NCCTs) have reshaped the field of software development, which accurately suggest contextually-relevant code snippets benefiting from language modeling techniques. However, language models may emit the training data verbatim during inference with appropriate prompts. This memorization property raises privacy concerns of commercial NCCTs about the hard-coded credential leakage, leading to unauthorized access to systems. Therefore, to answer whether NCCTs will inadvertently emit the hard-coded credential, we propose an evaluation tool called Hard-coded Credential Revealer (HCR). HCR effectively constructs test prompts from GitHub code files with credentials to trigger memorization phenomenon of commercial NCCTs. Then, HCR extracts credentials with pre-defined format from the responses by four designed filters. We apply HCR to evaluate two representative commercial NCCTs: GitHub Copilot and Amazon CodeWhisperer and successfully extracted 2,702 hard-coded credentials from Copilot and 129 secrets from CodeWhisper under the black-box setting, among which at least 3.6% and 5.4% secrets are real strings from GitHub repositories. Moreover, two operational credentials were identified. The experimental results raise the severe privacy concern of the potential leakage of hard-coded credentials in the training data of commercial NCCTs

Relationship between Microstructure and Properties of Cu-Cr-Ag-(Ce) Alloy Using Microscopic Investigation

Microstructure, precipitation hardening response, and mechanical and physical properties of Cu-Cr-Ag alloy and Cu-Cr-Ag-Ce alloy have been investigated using transmission electron microscopy, scanning electron microscope, optical microscope, electrical conductivity analysis, and tensile test. The influence of element Ce on the matrix refinement, impurity removal, and precipitation in the Cu-Cr-Ag alloys has been analyzed. The experimental results show that the strength and electrical conductivity of Ce containing alloys are greater than those of Ce-free alloys after each processing step. Improvement of strength and electrical conductivity of the Cu-Cr-Ag alloy by adding Ce element is attributed to removing oxygen and sulfur from as-cast alloy

Snake‐Inspired, Nano‐Stepped Surface with Tunable Frictional Anisotropy Made from a Shape‐Memory Polymer for Unidirectional Transport of Microparticles

The ventral scales of many snake species are decorated with oriented micro‐fibril structures featuring nano‐steps to achieve anisotropic friction for efficient locomotion. Here, a nano‐stepped surface with tunable frictional anisotropy inspired by this natural structure is presented. It is fabricated by replicating the micro‐fibril structure of the ventral scales of the Chinese cobra (Naja atra) into a thermo‐responsive shape‐memory polymer via hot embossing. The resulting smart surface transfers from a flat topography to a predefined structure of nano‐steps upon heating. During this recovery process, the nano‐steps grow out of the surfaces resulting in a surface with frictional anisotropy, which is characterized in situ by an atomic force microscopy. The desired frictional anisotropy can be customized by stopping the heating process before full recovery. The nano‐stepped surface is employed for the unidirectional transport of microscale particles through small random vibrations. Due to the frictional anisotropy, the microspheres drift unidirectionally (down the nano‐steps). Finally, dry self‐cleaning is demonstrated by the transportation of a pile of microparticles

Validating Multimedia Content Moderation Software via Semantic Fusion

The exponential growth of social media platforms, such as Facebook and TikTok, has revolutionized communication and content publication in human society. Users on these platforms can publish multimedia content that delivers information via the combination of text, audio, images, and video. Meanwhile, the multimedia content release facility has been increasingly exploited to propagate toxic content, such as hate speech, malicious advertisements, and pornography. To this end, content moderation software has been widely deployed on these platforms to detect and blocks toxic content. However, due to the complexity of content moderation models and the difficulty of understanding information across multiple modalities, existing content moderation software can fail to detect toxic content, which often leads to extremely negative impacts. We introduce Semantic Fusion, a general, effective methodology for validating multimedia content moderation software. Our key idea is to fuse two or more existing single-modal inputs (e.g., a textual sentence and an image) into a new input that combines the semantics of its ancestors in a novel manner and has toxic nature by construction. This fused input is then used for validating multimedia content moderation software. We realized Semantic Fusion as DUO, a practical content moderation software testing tool. In our evaluation, we employ DUO to test five commercial content moderation software and two state-of-the-art models against three kinds of toxic content. The results show that DUO achieves up to 100% error finding rate (EFR) when testing moderation software. In addition, we leverage the test cases generated by DUO to retrain the two models we explored, which largely improves model robustness while maintaining the accuracy on the original test set.Comment: Accepted by ISSTA 202

Control of cationic amino acid transport and retroviral receptor functions in a membrane protein family

A partial cDNA sequence indicated that the T lymphocyte early-activation gene (Tea) encodes a protein related to the dual-function ecotropic retrovirus receptor/cationic amino acid transporter (ecoR/CAT1), and RNA blots suggested highest Tea expression in T lymphocytes and liver (MacLeod, C.L., Finley, K., Kakuda, D. Kozad, C.A., and Wilkinson, M.F. (1990) Mol. Cell. Biol. 7, 3663-3674). The sequence of full-length Tea cDNA from liver (3683 bases) predicts a 657-amino-acid protein (CAT2 alpha) with 12-14 transmembrane domains. A long (515 base) region with six initiation codons and termination codons precedes the translation start codon. The liver Tea cDNA is identical to Tea cDNA from T lymphocytes (encoding CAT2 beta) with the exception of an apparent alternatively spliced sequence encoding a hydrophilic loop of 43 amino acids. The liver-specific sequence contains unique consensus sites for phosphorylation by cyclic AMP-dependent protein kinase and by protein kinase C. Injection of Xenopus oocytes with CAT2 alpha or CAT2 beta messenger RNA resulted in expression of Na(+)-independent cationic amino acid transport that was detected by current measurements under voltage-clamp. Although the amino acid sequences of the isoforms differ in only 21 of 43 residues with the majority of substitutions being conservative, the apparent affinity of CAT2 beta for arginine uptake was 70-fold higher than the CAT2 alpha isoform (Km 38 microM versus 2.7 mM). Neither isoform functioned as a receptor for ecotropic or amphotropic murine retroviruses. However, CAT1-CAT2 chimeric proteins that contain the first three putative extracellular loops of ecoR/CAT1 functioned as ecotropic receptors despite a diminished capacity to bind the viral envelope glycoprotein. The chimeric proteins also functioned as basic amino acid transporters with substrate affinities corresponding to the CAT2 isoform constituting the carboxyl-terminal portion. These results demonstrate that domains of these transporters can function in chimeric combinations to control viral receptor and transport functions